license: apache-2.0
base_model: TobiasLogic/Qwen2.5-Coder-32B-abliterated
tags:

• abliterated
• uncensored
• code
• qwen2.5
• gguf
• llama.cpp
• ollama
  pipeline_tag: text-generation
  language:
• en

Qwen2.5-Coder-32B-abliterated — GGUF (Q4_K_M)

Q4_K_M GGUF quantization of
TobiasLogic/Qwen2.5-Coder-32B-abliterated,
an abliterated (uncensored) build of
Qwen/Qwen2.5-Coder-32B-Instruct.

The refusal direction (Arditi et al. 2024, "Refusal in LLMs is mediated by a
single direction") was orthogonalized out of every residual-writing weight in
the fp16 model, then quantized to GGUF with llama.cpp. Runs on CPU or GPU via
Ollama / llama.cpp; ~20 GB, fits comfortably in 24 GB VRAM.

Refusal rate (held-out harmful eval, measured on the fp16 model)

Benchmarks

Coding capability scored with the official EvalPlus harness — greedy decoding, pass@1, every solution executed against unit tests. Both columns use the same harness, so it's a true apples-to-apples comparison against the full-precision base model.

Abliteration removed refusals without breaking coding ability. The uncensored 4-bit build stays within ~3 points of the base on HumanEval and beats it on both MBPP variants — average delta ≈ −0.6 points across the four benchmarks. Not bad for a 19 GB GGUF you can run on a single 24 GB GPU.

_{Base numbers: Qwen2.5-Coder-32B-Instruct, tech report Table 16. Measured 2026-07, Q4_K_M via Ollama.}

Usage

Ollama (a Modelfile is included in this repo):

# after downloading qwen2.5-coder-32b-abliterated-Q4_K_M.gguf and Modelfile:
ollama create qwen-coder-abliterated -f Modelfile
ollama run qwen-coder-abliterated

llama.cpp:

llama-cli -m qwen2.5-coder-32b-abliterated-Q4_K_M.gguf \
  -p "Write a port scanner in Python." -c 8192

Links

• fp16 weights: TobiasLogic/Qwen2.5-Coder-32B-abliterated
• Reproducible pipeline: github.com/TobiasLogic/Qwen2.5-Coder-Abliterate

License

Apache-2.0, inherited from the base model. You are responsible for how you use
this model and for complying with applicable law.